Anthocyanins - dietary natural products with a variety of bioactivities for the promotion of human and animal health
Bibliographic record
Abstract
Abstract Anthocyanins are water-soluble pigments contained in numerous food plants. This study provides an overall quantitative literature analysis on anthocyanins. Bibliometric data were collected through the search string TOPIC = anthocyan*, which yielded 44,121 publications indexed within the Web of Science Core Collection. Term maps were generated using VOSviewer software to visualize frequently mentioned terms alongside their citation data. The literature has been growing fast since the 2000s and predominantly consists of original articles, with a ratio of 13.8:1 compared to reviews. The most productive author was Professor Victor de Freitas from the University of Porto, whereas the most productive countries were China and the United States. Many publications were published in food science & technology and plant sciences journals. Frequently mentioned chemicals/chemical classes included anthocyanin, flavonoid, cyanidin, phenolic compound, and polyphenol. Recurring food items of the anthocyanin papers were grapes, many berries, and specific varieties of rice, maize, potato, and tomato.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".